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9,882 results for “Scale”
Visualization of particle-resolved simulations of aerosols on the regional scale
<p><span><span>In this study, we visualize the evolution of aerosol particles over Northern California using simulations from the WRF-PartMC model. We present two days of results, centering the numerical grid on the Bay Area with a horizontal resolution of 4-by-4-kilometers. Each grid cell contains approximately 5,000 computational particles to represent the aerosol.</span></span></p> <p><span><span>Much like snowflakes, aerosol particles exhibit immense diversity in size and chemical composition, continuously evolving as they travel through the atmosphere. Our particle-resolved simulation captures these processes with unprecedented detail, including the aerosol mixing state. This study is pioneering in its use of particle-resolved methods to simulate aerosols on a regional scale.</span></span></p> <div></div> <div> <div> <div></div> </div> </div> <p> </p>
Dataset for Quantifying Dissolved Organic Matter Scaling Relationships and Trends in Watersheds
<p>This updated dataset explores the relationship between species richness (i.e., compound metrics) and watershed characteristics using various environmental datasets and data-driven methods. The repository includes data and associated links, notes on curating the data (e.g., graphics, slides), and data analysis scripts to understand species-watershed relationships.</p>
Large-scale UK Biobank Whole Body Atlases
<p>Population atlases are commonly utilised in medical imaging to facilitate the investigation of variability across populations. <br>Such atlases enable the mapping of medical images into a common coordinate system, promoting comparability and enabling the study of inter-subject differences. <br>Constructing such atlases becomes particularly challenging when working with highly heterogeneous datasets, such as whole-body images, where subjects show significant anatomical variations.<br>In this work, we propose a pipeline for generating a standardised whole-body atlas for a highly heterogeneous population by partitioning the population into anatomically meaningful subgroups. <br>Using magnetic resonance (MR) images from the UK Biobank dataset, we create six whole-body atlases representing a healthy population average. <br>We furthermore unbias them, and this way obtain a realistic representation of the population.<br>In addition to the anatomical atlases, we generate probabilistic atlases that capture the distributions of abdominal fat (visceral and subcutaneous) and five abdominal organs across the population (liver, spleen, pancreas, left and right kidneys).<br>We demonstrate a clinical application of these atlases, using the differences between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects from the atlas space.<br>With this work, we make the constructed anatomical and label atlases publically available and anticipate them to support medical research conducted on whole-body MR images.</p>
Processed Data for Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology.
<p>These files include all data used to create Figures in Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology. Raw data provenances and methodological processing are cited in the published manuscript.</p> <p>See README file for metadata information.</p>
Fig. 4. A in New record of commensal scale worms, Arctonoe vittata (Grube, 1855) and Hyperhalosydna striata (Kinberg, 1856) (Polychaeta: Polynoidae) from Korean waters
Fig. 4. A, Hyperhalosydna striata in a tube of eunicid polychaeta; B, Freeliving H. striata.
Data for the paper "Small-scale variabilities in the electric field and the particle precipitation and their impacts on Joule heating"
<p>This dataset works specifically for the paper Zhu et al., (2018), Small-scale variabilities in the electric field and the particle precipitation and their impacts on Joule heating. </p> <p>Notes:</p> <p>1) fig1 file for data generating fig 1</p> <p>2) fig2 zip for data generating fig 2 </p> <p>3) fig3 file for data generating fig 3</p> <p>4) fig4 zip for data generating fig 4 (In that zip file, Run 1 for fig 4a, Run2 for fig 4b, Run3 for fig 4c)</p> <p> </p>
Supplementary material for publication "Pore-scale salinity effects on methane hydrate dissociation" by Almenningen et al
<p>Supplementary materials for publication "Pore-scale salinity effects on methane hydrate dissociation"</p> <p>Abstract: Sedimentary methane gas hydrates may become a significant source of methane gas in the global energy mix for the next decades. The widespread distribution of methane hydrates, primarily in subsea sediments on continental margins, makes the crystalline compound attractive for countries with shorelines that seek self-sustainable energy. Fundamental understanding of pore-level methane hydrate distribution and dissociation pattern is important to anticipate the gas production from hydrate reservoirs. Especially the effect of local salinity gradients on dissociation characteristics must be understood as the aqueous phase in most reservoirs is saline. We evaluate the pore-level salinity effect on hydrate dissociation experimentally using silicon-wafer micro-models capable of withstanding high internal pressures. Methane hydrates were formed with brines for a range of salinities (0.0, 2.0, 3.5 and 5.0 wt% NaCl), and we study hydrate dissociation during both depressurization and thermal stimulation, which currently are the most cost-effective production methods. The laboratory results show how initial pore-scale hydrate distribution prior to dissociation affect the melting and mobilization of gas. The local pore-water salinities influenced the stability of the hydrate structure, and led to distinct dissociation patterns due to water freshening.</p>
Large-scale full-length HTT Q19 and Q42 purification from HEK293T – 2018/04/16
<p>Huntingtin structure-function open lab notebook project. Large-scale full-length HTT Q19 and Q42 purification from HEK293T – 2018/04/16.</p>
Large-scale full-length HTT Q23 purification from HEK293T – 2018/04/30
<p>Huntingtin structure-function open lab notebook project. Large-scale full-length HTT Q23 purification from HEK293T – 2018/04/30.</p>
Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software
<p>Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software, SCAM'13.</p> <p>https://www.monperrus.net/martin/companion-diversity-api-usages</p>
Small scale test-expression of full-length huntingtin Q23 with HAP40 in baculoviral expression system production in sf9 insect cells – 2018/05/14
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment and rationale</strong> - Small scale test-expression of full-length huntingtin Q23 with HAP40 in baculoviral expression system production in sf9 insect cells – 2018/05/14. To validate our insect cell production system for the generation of physiologically relevant huntingtin constructs, HTT and HAP40 will be co-expressed and purified from sf9 culture. </p>
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
Data from: A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration
<p>This is a complete dataset for achieving results of the manuscript "<strong>A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration</strong>". </p> <p> </p> <p><strong>Abstract: </strong></p> <ol> <li>Achieving global targets for forest restoration will require cost-effective strategies to return agricultural land to forest, while minimizing implementation costs and negative outcomes for agricultural production.</li> <li>We present a landscape approach for optimizing the cost-effectiveness of large-scale forest restoration. Across three different landscapes within Brazil's Atlantic Forest biodiversity hotspot, we modelled landscape scenarios based on spatially-explicit data on the probability of natural regeneration, restoration costs, land opportunity costs, and forest restoration outcomes for increasing carbon stocking and landscape connectivity<em>.</em> We compare benefits of our cost-reduction approach to the legally mandated riparian restoration and randomly distributed approaches.</li> <li>Compared with riparian prioritization and considering both implementation and opportunity costs, our cost-reduction scenario produced the greatest savings (20.9%) in mechanized agricultural landscapes.</li> <li>When only considering implementation costs, our cost-reduction scenario led to the highest savings (38.4%) in the landscape with highest forest cover where natural regeneration potential is highest and enables cost-effective carbon stocking and connectivity.</li> </ol> <p><em>Synthesis and applications.</em> We present a guide for forest restoration planning that maximizes specific outcomes with minimal costs and reduction of agricultural production. Furthermore, we show how policies could encourage prioritization of low-cost restoration via natural regeneration, increasing cost-effectiveness. While our study focuses on Brazil’s Atlantic Forest, the approach can be parameterized for other regions.</p> <p><strong>Resumo:</strong></p> <ol> <li>Atingir metas globais para a restauração florestal exigirá estratégias economicamente viáveis para transformar terras agrícolas em floresta, minimizando custos de implementação e os resultados negativos para a produção agrícola.</li> <li>Apresentamos uma abordagem de paisagem para otimizar a relação custo-eficácia da restauração florestal em larga escala. Em três diferentes paisagens, no Bioma da Mata Atlântica, modelamos cenários baseados em dados espacialmente explícitos sobre a probabilidade de regeneração natural, custos de restauração, custos de oportunidade da terra e resultados de restauração florestal com o objetivo de aumentar o estoque de carbono e a conectividade da paisagem. Por fim, comparamos os benefícios da nossa abordagem de redução de custos com a tradicional abordagem de restauração da paisagem em zonas ripárias (áreas de preservação permanente) e abordagens de espacialidade aleatoriamente distribuídas.</li> <li>Comparado com a priorização ripária e considerando os custos de implementação e de oportunidade, nosso cenário de redução de custos produziu as maiores economias (20,9%) em paisagens agrícolas mecanizadas.</li> <li>Ao considerar apenas os custos de implementação, nosso cenário de redução de custos levou à maior economia (38,4%) na paisagem com maior cobertura florestal, onde o potencial de regeneração natural é maior e permite uma melhor relação de custo-oportunidade no estoque de carbono e na conectividade da paisagem.</li> </ol> <p><em>Síntese e aplicações.</em> Apresentamos aqui um guia para o planejamento de restauração florestal que maximiza resultados específicos com redução de custos e mínima influência na produção agrícola. Além disso, mostramos como políticas públicas poderiam incentivar a priorização da restauração de baixo custo via regeneração natural, aumentando a relação custo-benefício. Enquanto nosso estudo se concentra na Mata Atlântica do Brasil, a abordagem pode ser parametrizada para outras regiões.</p>
Raw data for figures used in manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure. </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p> - Folders for the different experiments with the name expt[1..30]</p> <p> - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p> - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p> - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p> - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p> - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - README.txt</p> <p>3. fig5.tar.gz</p> <p> - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p> - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary08.tar.gz</p> <p> - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart12.tar.gz</p> <p> - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p> - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p> - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p> - README.txt (information on the image processing tutorial). </p>
Bubble and ppt data set at CIEM large scale wave flume
<p>The present work was developed in work of an internal UPC project. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona.</p> <p>The data set aims to evaluate the false reading of Optical Backscatter Sensors produced by air bubbles at breaking conditions. In order to do that a set of different wave conditions (wave height ranging from 0.2 up to 0.85 and period from 2 to 7 s) have been tested at two different Still water conditions (2.5 and 2.65) with a barred profile in the wave flume.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p>
Fig. 17. Zentamyia nigracinctus (Roberts, 1929), ♀ allotype (ANIC No. 29-009842): (a) dorsal; (b) lateral; (c) head, dorsal; (d) head, lateral; (e) wing; (f ) head, frontal; (g) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/journals/1876312x.
Fig. 17. Zentamyia nigracinctus (Roberts, 1929), ♀ allotype (ANIC No. 29-009842): (a) dorsal; (b) lateral; (c) head, dorsal; (d) head, lateral; (e) wing; (f ) head, frontal; (g) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/journals/1876312x.
Fig. 14. Eusurbus glossus sp. nov., ♂ holotype (QM T234845): (a) dorsal; (b) lateral; (c) head dorsal; (d) flagellum; (e) head, frontal; (f ) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/ journals/1876312x.
Fig. 14. Eusurbus glossus sp. nov., ♂ holotype (QM T234845): (a) dorsal; (b) lateral; (c) head dorsal; (d) flagellum; (e) head, frontal; (f ) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/ journals/1876312x.
Fig. 15. Eusurbus glossus sp. nov., ♂ paratype (QM T234850). Genitalia: (a) genital capsule, dorsal; (b) genital capsule, ventral; (c) genital capsule, lateral; (d) epandrium, dorsal; (e) epandrium, posterior; (f ) epandrium, lateral. Scale bars = 0.1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/journals/ 1876312x.
Fig. 15. Eusurbus glossus sp. nov., ♂ paratype (QM T234850). Genitalia: (a) genital capsule, dorsal; (b) genital capsule, ventral; (c) genital capsule, lateral; (d) epandrium, dorsal; (e) epandrium, posterior; (f ) epandrium, lateral. Scale bars = 0.1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/journals/ 1876312x.
Fig. 12. Eusurbus danielsi sp. nov. ♀ (ANIC No. 29-038691): (a) dorsal; (b) lateral; (c) flagellum; (d) head, frontal; (e) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/journals/1876312x.
Fig. 12. Eusurbus danielsi sp. nov. ♀ (ANIC No. 29-038691): (a) dorsal; (b) lateral; (c) flagellum; (d) head, frontal; (e) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/journals/1876312x.
Fig. 9. Eusurbus crassilabris Macquart, 1855. ♀ (QM): (a) dorsal; (b) lateral; (c) head, dorsal; (d) head, lateral; (e) head, frontal; (f ) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/ journals/1876312x.
Fig. 9. Eusurbus crassilabris Macquart, 1855. ♀ (QM): (a) dorsal; (b) lateral; (c) head, dorsal; (d) head, lateral; (e) head, frontal; (f ) head, profile. Scale bars = 1 mm. This figure is published in colour in the online edition of this journal, which can be accessed via http://booksandjournals.brillonline.com/content/ journals/1876312x.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.